Quick Answer
The three best AI tools for commercial real estate in 2026 are Primer (document intelligence and template mapping for acquisition teams), RedIQ (multifamily data extraction with a proprietary comp database), and Dealpath (enterprise deal pipeline management). The right choice depends on whether your bottleneck is document processing, pipeline tracking, or lending automation.
What can AI do in commercial real estate?
AI in CRE handles the high-volume, repetitive tasks between document receipt and investment decision: data extraction, document parsing, conflict detection, and pipeline tracking. According to JLL's 2025 research, 92% of CRE teams have started piloting AI, yet only 5% report achieving most of their program goals. The gap is usually the wrong tool for the right job.
Deloitte's 2026 CRE Outlook found that 76% of CRE firms are already exploring or implementing AI. McKinsey estimates AI could generate $110 to $180 billion in value for real estate, with early adopters reporting 15 to 20% ROI on their AI investments.
| Task category | What AI handles today | Still requires humans |
|---|---|---|
| Document processing | Extract rent rolls, T12s, OMs; flag missing data | Judgment calls on data quality; broker relationship |
| Underwriting setup | Populate Excel models; reconcile conflicting numbers | Assumption setting; market thesis; IC narrative |
| Deal screening | Score deals vs. criteria; surface key metrics instantly | Go/no-go decision; off-market relationship deals |
| Pipeline management | Track deal status; assign tasks; notify on deadlines | LOI negotiation; LP communication; legal review |
| Portfolio monitoring | Stress-test DSCR; flag covenant breaches; benchmark | Asset strategy; capex allocation; disposition timing |
The NAIOP winter 2025 report on AI's growing impact notes that the biggest bottleneck is not model sophistication but data structure: most CRE documents are unstructured PDFs, spreadsheets in non-standard formats, and Yardi or RealPage exports that differ by property management company. Purpose-built CRE AI tools solve this; general AI tools do not.
The 8 best CRE AI tools compared
These tools fall into three distinct categories: document intelligence (extracts and structures data from CRE documents), deal management (tracks pipeline status and team workflow), and lending automation (built for CRE lenders and credit teams). Many teams use one tool from each category.
| Tool | Category | Best for | Asset classes | Cites sources | Your Excel model |
|---|---|---|---|---|---|
| Primer (PropRise) | Document intelligence | Acquisition teams, any asset class | All | Y | Y |
| RedIQ | Document intelligence | Multifamily brokers and buyers | Multifamily, SFR | No | Via plugin |
| Dealpath | Deal management | Enterprise pipeline tracking | All | N/A | N/A |
| Blooma | Lending automation | CRE lenders and credit teams | All (lender view) | N/A | N/A |
| Coyote Software | Portfolio management | Institutional asset managers (UK/Europe) | All | N/A | N/A |
| HelloData | Comp intelligence | Rent comp research | Multifamily | N/A | N/A |
| DIY: ChatGPT / Claude | General AI | Memos, summaries, research | All (limited) | No | No |
Primer by PropRise
Primer is a document intelligence platform built for CRE acquisition and underwriting teams. It extracts data from any offering memorandum, rent roll, T12, or Yardi export, maps the results directly into your existing Excel model, and cites every output back to its source document, page number, and table.
The core differentiator is reconciliation intelligence. When numbers conflict across documents (for example, when the OM's stated NOI differs from what the T12 actually shows), Primer surfaces the conflict instead of silently picking one. Every cell links to its source, so your IC can audit any number in seconds. Live in 48 hours; no rekeying required to onboard your model.
Strengths
- + Works with any document format or asset class
- + Maps into your existing Excel model, not its own
- + Cites every output to source document and page
- + Flags conflicts when documents disagree
- + Excel plugin places data directly into your template
- + Live in 48 hours; no migration or onboarding delay
Limitations
- - Does not have RedIQ's decade-long multifamily comp database
- - Not a pipeline management tool (pairs best with a CRM or Dealpath)
- - Lending-side automation is not the primary use case
RedIQ
RedIQ (by Radix) is the established standard for multifamily data extraction, trusted by acquisition teams, brokers, and lenders for over a decade. Its core product, dataIQ, extracts and standardizes rent rolls and operating statements with drag-and-drop simplicity. QuickSync places that data directly into any Excel template.
RedIQ's real moat is its proprietary comparables database, built from 500+ deal submissions over 10 years. For multifamily buyers who need historical deal comps quickly, this data advantage is significant. The platform's valuationIQ model handles full underwriting scenarios with customizable assumptions.
Strengths
- + 10+ years of multifamily comp data
- + Fast: raw files to full underwriting in 30 minutes
- + QuickSync Excel plugin is widely used and familiar
- + Established integrations with brokers and lenders
Limitations
- - Primarily limited to multifamily and SFR asset classes
- - Does not cite sources or flag cross-document conflicts
- - Template remapping required per deal cycle
- - Limited to T12 and rent roll document types; less flexible on OMs
Dealpath
Dealpath is the leading enterprise deal management platform for CRE, focused on pipeline tracking, team workflow, and reporting across parallel deal tracks. In 2024, Dealpath announced a strategic partnership with CBRE Capital Markets and launched AI Studio, which includes an AI Extract module that abstracts OM data in under one minute with 95% stated accuracy.
Dealpath solves the "who has the ball" problem at scale: across 5 to 10 simultaneous deals, it tracks task ownership, deadlines, and deal stage for every team member. The BI-style dashboard reporting provides fund-level portfolio analytics. Dealpath Connect integrates with brokerage listings to ingest new opportunities directly.
Strengths
- + Best-in-class pipeline tracking across parallel deals
- + CBRE Capital Markets integration (Dealpath Connect)
- + AI Studio for OM abstraction and deal screening
- + Supports lenders and equity investors
- + Enterprise-grade permissions and audit logging
Limitations
- - Designed for large teams; overkill for sub-10-person shops
- - AI Extract does not map into your custom Excel model
- - Enterprise pricing; requires formal procurement
- - Longer onboarding compared to lighter tools
See Primer handle your next OM
Upload your own deal: Primer extracts from the OM, rent roll, and T12, maps results into your model, and cites every cell to its source. 20-minute demo.
Book a 20-min demoBlooma
Blooma is an AI-powered CRE lending platform designed for commercial banks, credit unions, and debt funds. It automates roughly 80% of the pre-flight underwriting process for loan origination, analyzing over 5,000 data points per deal against the lender's credit policy and returning a structured analysis in minutes.
Blooma's portfolio monitoring module allows lenders to stress-test their entire book against rate changes, cap rate expansion, and vacancy shifts. The platform's stated accuracy rate is 99% on document data ingestion, and it integrates with third-party data providers for real-time market data. According to Blooma, underwriters using the platform can process up to 400% more deals.
Strengths
- + Built specifically for CRE lenders, not equity buyers
- + Portfolio-level stress testing on DSCR, LTV, Debt Yield
- + Real-time third-party data integrations
- + Significant throughput gains for high-volume lenders
Limitations
- - Not designed for equity acquisition underwriting
- - Does not map into custom Excel models
- - Primarily relevant to institutional lenders with loan portfolios
- - Enterprise implementation timeline
Coyote Software (now InvestorFlow)
Coyote Software is a cloud-based CRE CRM and asset management platform used by over 50 institutional firms, primarily in the UK and Europe. In April 2024, InvestorFlow acquired Coyote to create a combined industry cloud covering the full deal lifecycle from LP relations to portfolio monitoring.
Three of the top five real estate investors in Europe (combined AUM exceeding £200 billion) use Coyote to manage their front office. The platform integrates data from Yardi, MRI, Infabode, and WiredScore into a single consolidated dashboard across 80,000 assets and 500 million square feet of real estate.
Strengths
- + Deep Yardi and MRI integrations
- + Proven at institutional scale (£200B+ AUM clients)
- + Covers deal flow, asset management, and portfolio risk
- + Post-acquisition via InvestorFlow combination
Limitations
- - Primarily UK and European market focus
- - Not a document intelligence tool for OM extraction
- - Enterprise-only; not suited to emerging managers
- - Product roadmap in flux following InvestorFlow acquisition
HelloData
HelloData is a multifamily comp intelligence platform: it pulls rent, occupancy, and unit mix data for comparable properties and surfaces it in a clean dashboard. It is not an underwriting or document extraction tool; it is a market data tool for the comp analysis step of underwriting.
At approximately $250 per month, HelloData is frequently used as a supplement to (not a replacement for) underwriting tools. It competes more directly with CoStar and Yardi Matrix on the comp data side than with Primer or RedIQ on the document intelligence side.
Strengths
- + Affordable entry point (~$250/mo)
- + Clean, fast comp search interface
- + Good for quick rent comp validation
Limitations
- - Does not extract or process deal documents
- - Multifamily only; no self-storage, industrial, or hotel data
- - A comp tool, not an underwriting tool
- - Does not replace data entry or model population
DIY approach: ChatGPT and Claude
General-purpose AI assistants (ChatGPT, Claude, Gemini) cost $20 to $25 per month and are genuinely useful for certain CRE tasks: drafting IC memos, summarizing OM narratives, answering market research questions, and generating analysis frameworks. Many analysts use them daily.
The hard limits: general AI tools cannot reconcile conflicting numbers across multiple CRE documents, do not maintain persistent Excel templates across deals, produce no audit trail linking outputs back to source pages, and frequently hallucinate specific financial figures. For the data-entry step (populating your underwriting model from the OM, T12, and rent roll), the DIY approach still requires an analyst to re-key by hand.
Useful for
- + Drafting IC memos and investment summaries
- + Summarizing long OM narratives
- + Market and submarket research
- + Very low cost ($20-25/mo)
Not suitable for
- - Populating your Excel model reliably
- - Reconciling conflicting numbers across documents
- - Providing an audit trail for IC review
- - Persistent template memory across deals
- - Producing numbers you can stake IC approval on
How to choose the right CRE AI tool
Start by identifying your primary bottleneck. Most CRE teams have one of three core problems, and the right tool category follows directly from the diagnosis.
Identify your actual bottleneck
Is it data entry (re-keying OMs, rent rolls, and T12s into your model)? Or is it pipeline visibility (not knowing which deals are at what stage)? Or is it comp research (getting market rents quickly)? Most teams conflate these and buy the wrong tool.
Count your deal volume and asset classes
A team looking at 5 multifamily deals per week has different needs than a team looking at 15 mixed-asset deals. Higher volume justifies more automation. Mixed asset classes (multifamily plus storage plus industrial) eliminate RedIQ as an option; you need a tool that works across all document types.
Test with one of your real deals
Never evaluate a CRE AI tool on the vendor's demo document. Ask to run a recent deal you already know the answer to: upload your own OM, your own rent roll, and your own T12. Verify that the output matches what you already know. Check whether numbers are cited. This exposes issues that marketing materials do not.
Calculate the ROI against analyst time
Use this formula to estimate your current cost of manual underwriting setup:
x $50/hr loaded cost x 52 weeks
= Annual cost of manual data entry
A two-analyst team reviewing 10 deals per week, spending 1.5 hours on data entry per deal, costs approximately $78,000 per year in analyst time on data entry alone, before any value-add work begins.
Check onboarding time and auditability
Ask two specific questions before signing: (a) How long to go live with our existing model? and (b) Can we trace any output number back to its source document? If onboarding takes months or if outputs cannot be audited, those are deal-breakers for IC-level work. The JLL future of AI in CRE research found that trust and auditability are the top adoption barriers at institutional firms.
Quick selection matrix
| Your situation | Recommended starting point |
|---|---|
| Acquisition team, any asset class, wants analyst-level extraction with citations | Primer |
| Pure multifamily buyer or broker; need comp database and fast extraction | RedIQ |
| Enterprise team managing 20+ simultaneous deals across fund(s) | Dealpath |
| CRE lender or credit fund needing automated loan origination | Blooma |
| Institutional asset manager (UK / Europe) needing portfolio CRM | Coyote / InvestorFlow |
| Need quick multifamily rent comps at low cost | HelloData |
| Need help drafting memos and market summaries; not extracting data | ChatGPT or Claude (DIY) |